Advances in Electric Power and Energy. Группа авторов

received a BE degree from Tsinghua University in 2006 and MSc and PhD degrees from the University of Minnesota in 2009 and 2012, all in Electrical Engineering. Her current research interests include power grid monitoring, distribution system operations and control, and energy data analytics. She received the NSF CAREER Award in 2017, the Siebel Energy Institute Seed Grant Award and the US AFRL Summer Faculty Fellowship in 2016.

CHAPTER 1 GENERAL CONSIDERATIONS

       Mohamed E. El‐Hawary

       Dalhousie University in Halifax, Nova Scotia, Canada

      In this introductory chapter, we introduce the concept of state estimation (SE) in electric power system and trace its evolution from a historical perspective. SE emerged as an indispensable real‐time tool that is part of a suite of applications designed to support and enable electric power operators' “situational awareness.” The term “situational awareness” in the context of power grid operation is “understanding the present environment and being able to accurately anticipate future problems to enable effective actions.”

      This chapter offers a detailed discussion of the role of SE in practice. A guide to the chapters included in this volume is offered to conclude the chapter.

1.1 PRELUDE

      At the IEEE Power Industry Computer Applications (PICA) conference held on 18–21 May 1969 in Denver, Colorado, Professor Fred C. Schweppe and his associates presented a three‐part paper on static state estimation and related detection and identification problems in electric power systems. The papers were subsequently published in the IEEE Transactions on Power Apparatus and Systems [1–3]. The first paper [1] introduced the overall problem statement, mathematical modeling, and general algorithms for state estimation, detection, and identification (SEDI) using weighted least squares (WLS) approximations. The second paper [2] discussed an approximate mathematical model and the resulting simplifications in SEDI. The third paper [3] dealt with implementation problems, considerations of dimensionality, execution speed and storage, and the time-varying nature of actual power systems.

A year later, Merrill and Schweppe [4] introduced a bad data suppression (BDS) estimator, which is computationally very similar to WLS approximation. The concept is no more complex, and bad data detection and identification can be performed “for free,” since BDS requires no more computer time or complexity than does WLS, and in the absence of bad data, BDS reduces to WLS.

1.2 DEFINING SSE

      In 1974, Schweppe and Handschin [5] described state estimation (SE) using the following metaphor: “The life blood of the control system is a base of clean pure data defining the system state and status (voltages, network configuration). This life blood is obtained from the nourishment provided by the measurements gathered from around the system (data acquisition). A static state estimator is the digestive system which removes the impurities from the measurements and converts them into a form which the brain (man or computer) of the central control system can readily use to make ‘action’ decisions on system economy, quality, and security.”

      Reference [1] formally defines the static state of an electric power system as the vector of voltage magnitudes and angles at all network buses. The static state estimator (SSE) is a data processing algorithm for converting imperfect redundant meter readings and other available information to an estimate of the static state.

      Item 603‐02‐09 of the International Electrotechnical Commission (IEC) Electropedia [6] offers the following definition of “state estimation” as “the computation of the most probable currents and voltages within the network at a given instant by solving a system of mostly nonlinear equations whose parameters are obtained by means of redundant measurements.”

      The North American Electric Reliability Corporation (NERC) Real‐Time Tools Best Practices Task Force (RTBPTF) 2008 final report [7] offers the following definition: “A state estimator is an application that performs statistical analysis using a set of imperfect, redundant, telemetered power‐system data to determine the system's current condition. The system condition or state is a function of several variables: bus voltages, relative phase angles, and tap changing transformer positions. A state estimator can typically identify bad analog telemetry, estimate non‐telemetered flows and voltages, and determine actual voltage and thermal violations in observable areas.”

      According to [5], SSE has evolved rapidly to online implementations beginning with the Norwegian Tokke installation [8] followed by the larger AEP installation [9] soon after. Not long later, T. E. Dy Liacco [10] stated: “Although the number of control centers with State Estimation is still rather small, the number is increasing at a rapid rate. The requirement for State Estimation at a modern control center has become the rule, rather than the exception.”

      The fundamental problem of state estimation can be defined as an over determined system of nonlinear equations solved as an unconstrained weighted least squares (WLS) minimization problem. The WLS estimator minimizes the weighted sum of the squares of the residuals. Residuals are the error or difference between the estimated and the actual values [11]. Many papers and books treat the broad generic area of “state estimation” in system theory [12–14]. State estimation concepts can be applied in other power systems areas [15–20].

1.3 THE NEED FOR STATE ESTIMATION

      Security control is the main strategy used in the operation of electric power systems, where actions are taken to prevent an impending emergency, to correct an existing emergency, or to recover from an emergency. Knowing the state of the system under steady‐state conditions is the key to security control.

      Control centers may be classified into two types, according to the information base available. In one type of control center, the raw power system data as obtained in real time is an adequate information base for operation. The other type of control center goes beyond the mere acquisition of data. By applying state estimation, a far better and a more comprehensive information base than raw data is obtained.

      The importance of the real‐time load flow fed by state estimation lies in its use as basis for security analysis. With the load flow as a base, reference allows analyzing the effects on the system of any contingency event. In contrast, without state estimation, there is not much to be done with raw data except to check it for abnormal values.

      A further important feature of state estimation is the ability to detect the presence of bad data (outliers) and to identify which data is in error. Corrections can then be expedited in the field on the faulty instrumentation. Without state estimation, there is no effective, systematic way of finding measurement errors. Some sort of data validation has been attempted wherein power measurements around a bus are summed up and flows at both ends of a branch checked against each other, but these checks apart from being inconclusive end up being too complicated as it has to take into account the topology of the network. Now network topology is handled systematically and correctly by state estimation. Hence for all the checking done by so‐called data validation programs, it is best to go directly to state estimation.

      In the modeling of power systems for security control functions, there are usually external networks, i.e. networks or subnetworks, which are not being telemetered by the control center and which are not observable. There are two approaches for estimating the state of these external networks. One approach is to use pseudo‐measurements, based on statistics and forecasts, of the injections at the nodes of the external network. The pseudo-measurements are then assigned relatively low weights and included as part of the measurement set in the state estimation routine. The second approach is to perform the state estimation only on the observable part. The state of the external network is then obtained by finding a load flow solution using the pseudo-measurements as inputs with the boundary node voltages held at the values determined by the state estimation of the observable part.

      For